Computational protocol: Identified single nucleotide polymorphisms and haplotypes at 16q22.1 increase diabetic nephropathy risk in Han Chinese population

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Protocol publication

[…] Genomic DNA from peripheral blood was prepared using the Puregene DNA isolation kit (Gentra Systems, Minneapolis, MN, USA). Our samples were genotyped using Illumina HumanHap550-Duo BeadChip, which was performed by deCODE Genetics (Reykjavík, Iceland). Genotypes were called using the standard procedure in BeadStudio (Illumina, Inc., San Diego, CA, USA), with the default parameters recommended by the platform manufacturer. The genotyping quality control procedures used to identify and remove poor-quality data were described previously []. Individual SNPs were excluded if they had a total call rate <95% across all individuals, a minor allele frequency <5% and a total call rate <99%, or had significant deviation from Hardy-Weinberg disequilibrium (p-value <10−7) in these subjects. Further details of genotyping quality control procedures are available in the published study []. After SNPs quality control, 429,018 SNPs were used and their total call rate was 99.9%. We also examined population stratification by using multidimensional scaling (MDS) analysis as implemented in PLINK. The results of MDS analysis showed that there was no evidence for population stratification. [...] Demographic and clinical characteristics of study subjects were examined, including sex, age, diabetes-related variables, behaviors, biochemical variables, and history of diseases. Continuous variables are reported as mean ± standard deviation (SD), and categorical variables are reported as number and percentage. Two-sample t tests and Chi-square tests were used for the bivariate analyses. Because the distribution of the triglycerides was skewed, the data were normalized using a natural log-transformation, and the geometric mean ± SD was calculated. To identify the DN susceptibility variants, single-SNP association tests using Cochran-Armitage trend test were performed. Then, multiple logistic regression analysis using an additive-effect disease model (an ordinal genotype model: 0, 1 and 2 of a minor allele) was performed for each SNP with adjustment of the subject's sex, age, BMI, and durations of diabetes. The Bonferroni correction was used to adjust for multiple comparisons. P-value less than 10−4, association was considered to be statistically significant. According to previously published studies, as well as the results of single-SNP association tests and the Manhattan plot in our current study, potential susceptibility regions for DN were selected to perform haplotype analysis. In haplotype-based association analysis, the sliding window approach was adopted to detect haplotype effects. The window sizes of 3-SNP, 4-SNP, and 5-SNP haplotypes were used. Each haplotype with a frequency of >0.05 in this population was analyzed. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were calculated to estimate the effect sizes of the identified SNPs and haplotypes. In addition, the linkage disequilibrium (LD) structures of the identified contiguous SNPs were examined. Pairwise LD was measured by the r2 statistic. For power calculation of our case–control study (217 DN cases and 357 controls), Quanto software [] was used. Under an additive effect disease model with a prevalence of 10% for DN (from our dataset), given a genetic relative risk of 1.85 and a disease allele frequency of 0.25-0.45, the power of our study was 0.76-0.86 at an alpha level of 10−4. All analyses were carried out using Haploview (v4.2) [], PLINK (v1.07) (pngu.mgh.harvard.edu/purcell/plink) [], and SAS (v9.3, SAS Institute Inc, Cary, NC, USA) software. The regional plot was plotted from the LocusZoom, a web-based plotting tool (csg.sph.umich.edu/locuszoom) []. The in silico prediction tool is-rSNP was used to predict potential regulatory SNPs (rSNPs) []. […]

Pipeline specifications

Software tools PLINK, Haploview, LocusZoom, is-rSNP
Application GWAS
Organisms Homo sapiens
Diseases Diabetes Mellitus, Diabetic Nephropathies, Kidney Failure, Chronic, Machado-Joseph Disease